Feature Scaling For EEG Human Concentration Using Particle Swarm Optimization

Feature Scaling For EEG Human Concentration Using Particle Swarm Optimization

Electroencephalograph (EEG) is a one of recording technique that is widely used to measure human activities through brain signals. One of actively growing research in the past years is to measure human concentration using EEG. Obtaining relevant features for recognizing human concentration state bec...

Full description

Bibliographic Details
Main Authors: K. G., Li, Mohd Ibrahim , Shapiai, Asrul, Adam, Zuwairie, Ibrahim
Format: Conference or Workshop Item
Language:English
English
Published: IEEE 2017
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/18259/
http://umpir.ump.edu.my/id/eprint/18259/
http://umpir.ump.edu.my/id/eprint/18259/1/Feature%20scaling%20for%20EEG%20human%20concentration%20using%20particle%20swarm%20optimization.pdf
http://umpir.ump.edu.my/id/eprint/18259/2/Feature%20scaling%20for%20EEG%20human%20concentration%20using%20particle%20swarm%20optimization%201.pdf
id ump-18259
recordtype eprints
spelling ump-182592018-03-21T07:39:16Z http://umpir.ump.edu.my/id/eprint/18259/ Feature Scaling For EEG Human Concentration Using Particle Swarm Optimization K. G., Li Mohd Ibrahim , Shapiai Asrul, Adam Zuwairie, Ibrahim TK Electrical engineering. Electronics Nuclear engineering Electroencephalograph (EEG) is a one of recording technique that is widely used to measure human activities through brain signals. One of actively growing research in the past years is to measure human concentration using EEG. Obtaining relevant features for recognizing human concentration state becomes a challenging task due to the nature of EEG signals is a non-stationary. In the past research, various combinations of features have been employed. However, to improve the classification performance, determining the importance of each employed feature is crucially needed. In this study, feature scaling method is introduced to assign different weights for important features. Four different features are investigated in frequency domain using wavelet transform (WT). Then, particle swarm optimization (PSO) is used to scale the features while extreme learning machine (ELM) is used to classify between concentration and non-concentration states. The recorded EEG signals from Neurosky Mindwave are used to evaluate the performance of the proposed technique. The final results indicate that the proposed technique offers higher performance accuracy as compared to the methods without feature scaling. IEEE 2017-02 Conference or Workshop Item PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/18259/1/Feature%20scaling%20for%20EEG%20human%20concentration%20using%20particle%20swarm%20optimization.pdf application/pdf en http://umpir.ump.edu.my/id/eprint/18259/2/Feature%20scaling%20for%20EEG%20human%20concentration%20using%20particle%20swarm%20optimization%201.pdf K. G., Li and Mohd Ibrahim , Shapiai and Asrul, Adam and Zuwairie, Ibrahim (2017) Feature Scaling For EEG Human Concentration Using Particle Swarm Optimization. In: International Conference on Information Technology and Electrical Engineering (ICITEE), 5 - 6 October 2016 , Yogyakarta, Indonesia. . ISBN 978-150904139-8 DOI: 10.1109/ICITEED.2016.7863292
repository_type Digital Repository
institution_category Local University
institution Universiti Malaysia Pahang
building UMP Institutional Repository
collection Online Access
language English
English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
K. G., Li
Mohd Ibrahim , Shapiai
Asrul, Adam
Zuwairie, Ibrahim
Feature Scaling For EEG Human Concentration Using Particle Swarm Optimization
description Electroencephalograph (EEG) is a one of recording technique that is widely used to measure human activities through brain signals. One of actively growing research in the past years is to measure human concentration using EEG. Obtaining relevant features for recognizing human concentration state becomes a challenging task due to the nature of EEG signals is a non-stationary. In the past research, various combinations of features have been employed. However, to improve the classification performance, determining the importance of each employed feature is crucially needed. In this study, feature scaling method is introduced to assign different weights for important features. Four different features are investigated in frequency domain using wavelet transform (WT). Then, particle swarm optimization (PSO) is used to scale the features while extreme learning machine (ELM) is used to classify between concentration and non-concentration states. The recorded EEG signals from Neurosky Mindwave are used to evaluate the performance of the proposed technique. The final results indicate that the proposed technique offers higher performance accuracy as compared to the methods without feature scaling.
format Conference or Workshop Item
author K. G., Li
Mohd Ibrahim , Shapiai
Asrul, Adam
Zuwairie, Ibrahim
author_facet K. G., Li
Mohd Ibrahim , Shapiai
Asrul, Adam
Zuwairie, Ibrahim
author_sort K. G., Li
title Feature Scaling For EEG Human Concentration Using Particle Swarm Optimization
title_short Feature Scaling For EEG Human Concentration Using Particle Swarm Optimization
title_full Feature Scaling For EEG Human Concentration Using Particle Swarm Optimization
title_fullStr Feature Scaling For EEG Human Concentration Using Particle Swarm Optimization
title_full_unstemmed Feature Scaling For EEG Human Concentration Using Particle Swarm Optimization
title_sort feature scaling for eeg human concentration using particle swarm optimization
publisher IEEE
publishDate 2017
url http://umpir.ump.edu.my/id/eprint/18259/
http://umpir.ump.edu.my/id/eprint/18259/
http://umpir.ump.edu.my/id/eprint/18259/1/Feature%20scaling%20for%20EEG%20human%20concentration%20using%20particle%20swarm%20optimization.pdf
http://umpir.ump.edu.my/id/eprint/18259/2/Feature%20scaling%20for%20EEG%20human%20concentration%20using%20particle%20swarm%20optimization%201.pdf
first_indexed 2023-09-18T22:25:46Z
last_indexed 2023-09-18T22:25:46Z
_version_ 1777415955308085248

Similar Items